BoolSat

BoolSat is a software tool and framework for solving boolean satisfiability problems. Given a propositional logic formula, BoolSat determines whether there exists an assignment of truth values to variables that makes the formula true. It typically accepts input in standard formats such as DIMACS CNF and can also operate on higher-level representations via translation routines. If a satisfying assignment exists, BoolSat can provide one; if not, it can produce a certificate of unsatisfiability in some implementations.

At its core, BoolSat uses modern SAT-solving techniques based on the DPLL paradigm with conflict-driven clause learning. Most practical implementations employ non-chronological backtracking, learned clause management, and heuristics for variable selection, such as activity-based schemes. Preprocessing and in-processing steps simplify formulas before or during solving, including unit propagation, subsumption, elimination of redundant literals, and constants folding. Many BoolSat engines support incremental solving and the use of assumptions to solve related problems efficiently, and some provide support for pseudo-Boolean constraints or cardinality constraints.

BoolSat has a wide range of applications in hardware and software verification, formal methods, model checking, automated test generation, planning, and combinatorial optimization. It is also used as a subroutine in higher-level tools such as constraint solvers and AI reasoning systems.

Although BoolSat is a generic term here, it reflects a class of solvers whose design emphasizes efficiency, scalability, and reliability on large, real-world instances. Its features are commonly found across many SAT solvers and are the subject of ongoing research to extend capabilities to quantified, weighted, or probabilistic variants.